Position control of an er valve-cylinder system via neural network controller

Choi, Seung‐Bok; Cheong, C.C.; Jung, J.M.; Choi, Young-Tai

doi:10.1016/s0957-4158(96)00034-7

Cited by 29 publications

(30 citation statements)

References 7 publications

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“…The results showed that ANN controller has lower power consumption of 8.1 percent and 6.6 percent than both PID and fuzzy controllers, respectively. Choi et al [10] have modeled a system of position control which uses a single-rod cylinder activated by an electrorheological (ER) valve. From the state-space model for the governing equations, a neural network control scheme has been synthesized to achieve the position control of the cylinder system.…”

Section: Introductionmentioning

confidence: 99%

Stem Control of a Sliding-Stem Pneumatic Control Valve Using a Recurrent Neural Network

Heidari

Homaei

2013

Advances in Artificial Neural Systems

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This paper presents a neural scheme for controlling an actuator of pneumatic control valve system. Bondgraph method has been used to model the actuator of control valve, in order to compare the response characteristics of valve. The proposed controller is such that the system is always operating in a closed loop, which should lead to better performance characteristics. For comparison, minimum-and full-order observer controllers are also utilized to control the actuator of pneumatic control valve. Simulation results give superior performance of the proposed neural control scheme.

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Section: Introductionmentioning

confidence: 99%

Stem Control of a Sliding-Stem Pneumatic Control Valve Using a Recurrent Neural Network

Heidari

Homaei

2013

Advances in Artificial Neural Systems

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“…Other works on fluid power using functional fluids are reported in the literature: ER valves and actuators were studied by H. T. Strandrud, A. J. Simmonds, Kondoh and Yokota (1999-a), Choi (Choi, et al 1997(Choi, et al , 1999, Wendt and Büsing (1998), Tanaka et al (1998), among others. Besides the works and researchers mentioned previously, there are other studies that have been done in the area of fluid power.…”

Section: Applications Of Er and Mr Fluids In Fluid Power Systemsmentioning

confidence: 97%

“…The use of micro ER valves has also been suggested by Kondoh and Yokota (1996, 1997-a, 1997-b, 1997. There are two types of micro ER valves: electrode-electrode type and orifice-electrode type, as shown in Fig.…”

Section: Valves and Actuatorsmentioning

confidence: 97%

“…The proposed micro three-port ER valve had a dynamic range of 0.35 MPa in the load pressure. Kondoh and Yokota (1996, 1997-a, 1997-b, 1997, and Kondoh et al (1996 built and studied the behaviour of micro ER valves of different sizes. They calculated the dimensions of the ER valve using equations (A-2) and (A-3) included in the Appendix.…”

Section: Valves and Actuatorsmentioning

confidence: 99%

See 1 more Smart Citation

An Overview of Magneto- and Electro-Rheological Fluids and their Applications in Fluid Power Systems

Agrawal¹,

Kulkarni²,

Vieira³

et al. 2001

International Journal of Fluid Power

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The rapid change in viscosity of magnetorheological (MR) and electrorheological (ER) fluids subjected to a magnetic or an electric field, respectively, has attracted the attention of many researchers. However, as MR fluids show higher yield stress than ER fluids, they have merited more attention during the last few years. In this paper we present an overview of magneto-and electrorheological fluids, their basic properties, behaviour under different flow types and their uses in fluid power systems, among others.

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“…A number of researchers have used neural network controllers [5,6] to overcome the nonlinear behaviour, with the advantage that no model of the device behaviour is required. However, the neural network requires training and its 'black box' nature may be unappealing to end users.…”

Section: Introductionmentioning

confidence: 99%

Limit Cycle Behavior of Smart Fluid Dampers Under Closed Loop Control

Sims

2005

Journal of Vibration and Acoustics

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Semi-active vibration dampers offer an attractive compromise between the simplicity and fail-safety of passive-devices, and the weight, cost and complexity of fully active systems. In addition, the dissipative nature of semi-active dampers ensures they always remain stable under closed loop control, unlike their fully-active counterparts. However, undesirable limit cycle behaviour remains a possibility which is not always properly considered during the controller design.Smart fluids provide an elegant means to produce semi-active damping, since their resistance to flow can be directly controlled by the application of an electric or magnetic field. However, the nonlinear behaviour of smart fluid dampers makes it difficult to design effective controllers, and so a wide variety of control strategies has been proposed in the literature. In general this work has overlooked the possibility of undesirable limit cycle behaviour under closed loop conditions. The aim of the present study is to demonstrate how the experimentally observed limit cycle behaviour of smart dampers can be predicted and explained by appropriate nonlinear models. The study is based upon a previously developed feedback control strategy, but the techniques described are relevant to other forms of smart damper control.2

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Position control of an er valve-cylinder system via neural network controller

Cited by 29 publications

References 7 publications

Stem Control of a Sliding-Stem Pneumatic Control Valve Using a Recurrent Neural Network

Stem Control of a Sliding-Stem Pneumatic Control Valve Using a Recurrent Neural Network

An Overview of Magneto- and Electro-Rheological Fluids and their Applications in Fluid Power Systems

Limit Cycle Behavior of Smart Fluid Dampers Under Closed Loop Control

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